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Embedded Computing on Arm at Anglia Ruskin University - UCAS

Course options

Course summary

Develop your understanding of embedded systems and advance your career with our 8-month online PG Cert. Advance your career in embedded systems and artificial intelligence (AI) with our Postgraduate Certificate in Embedded Computing. Study part-time by distance learning and develop the skills you need to harness the power of machine learning applications in various industrial contexts. Embedded computing, especially when paired with machine learning, promises to provide the tools to enhance technology, business models and decision-making across a range of sectors, from industrial automation, quality control, manufacturing, transport, banking and cyber security to health and social care. Our PG Cert Embedded Computing on Arm has been developed with industry leader Arm, the global leader in CPU technology, meaning you’ll benefit from industry expertise, training modules and real-life case studies. This PG Cert will give you the opportunity to explore the industry trends that emphasise embedded and portable devices optimised for machine learning at the edge. You will gain skills to leverage Arm technologies and develop intelligent, distributed, heterogeneous, and secure solutions. By working on real-life case studies with industry tools, you'll become proficient in embedded systems tools and techniques for machine learning on the edge applications for industry. In addition to the tuition fees, you'll also need to purchase some hardware, such as ST DISCO-L475E, and sensors, which we do not expect to exceed £100. You'll also need access to a fairly modern laptop or personal computer which runs Microsoft Windows 10 or later. Furthermore, admin rights are required to install relevant software packages. This course is also available as a full Master's in Embedded Computing and Machine Learning, or these modules may be taken on a module-by-module basis. Contact us for further details.

Modules

Core modules Embedded Systems Essentials with Arm IoT and Machine Learning at the Edge on Arm

Assessment method

We'll assess you in a number of ways including time-constrained assessments, coursework assignments and presentations. Our module case studies assess your ability to analyse situations, identify key issues, select, synthesise and apply techniques and skills, and evaluate the appropriateness of solutions when compared to industrial practice.


Entry requirements

Applicants will normally hold a first or second class first degree. While a prior degree in a subject containing computing or electronics is welcome, the course is open to applicants with an electronics / computing background and a passion for technology whose first degrees may be in other subjects. A Foundation degree in computing or electronics with an appropriate period of industrial experience may also be considered. Each applicant for the Master’s programme who possesses a Foundation degree will be expected to attend an interview where an assessment will be made to determine the standard of their industrial experience and suitability for the course.


Fees and funding

Tuition fees

No fee information has been provided for this course

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

No additional fees or cost information has been supplied for this course, please contact the provider directly.
Embedded Computing on Arm at Anglia Ruskin University - UCAS